Sometimes Less Is More: Romanian Word Sense Disambiguation Revisited

Recent approaches to Word Sense Disambigua- tion (WSD) generally fall into two classes: (1) information-intensive approaches and (2) information-poor approaches. Our hypothesis is that for memory-based learning (MBL), a re- duced amount of data is more beneficial than the full range of features used in the past. Our experiments show that MBL combined with a restricted set of features and a feature selection method that minimizes the feature set leads to competitive results, outperforming all systems that participated in the SENSEVAL-3 compe- tition on the Romanian data. Thus, with this specific method, a tightly controlled feature set improves the accuracy of the classifier, reach- ing 74.0% in the fine-grained and 78.7% in the coarse-grained evaluation.